Electric Stimulation

Chapter 1: Background: The search for effective electric stimulation protocols for peripheral nerve regeneration, specifically in dorsal root ganglion (DRG), is an ongoing area of interest. Multiple stimulation parameters using direct current, alternating current and pulsed magnetic electric fields have proven to increase neurite regeneration. In the past, there has been limited exploration of the impact of action potential-like electrical stimulation on DRG regeneration. New method: A novel action potential-like electrical stimuli output from a custom-built action potential generator board was used to assess multiple stimulation parameters on DRG regeneration. Finite-element modeling was used to determine electrolyte potential across a non-uniform electric field to test the effects of electric field strength from action potential-like stimuli on DRG regeneration. Total neurite length and neurite branching per DRG were examined for each applied field strength and frequency to determine the effects of action potential-like stimulation on DRG structural regeneration. Results: Action potential-like stimulation showed inhomogeneous distribution of neurite regeneration and branching with higher regeneration and branching seen in areas away from the electrodes compared to the nearly homogenous distribution seen from the controls. Whole well analysis showed significant increases in total neurite regeneration and branching across all stimulation conditions with electric field strength, particularly 40 V/m, having the strongest effect on DRG structural regeneration. Comparison with existing methods: This study provides preliminary evidence supporting the hypothesis that action potential-like electric fields can improve DRG regeneration. Conclusions: This system and method may have applications for clinical interventions aimed at rehabilitating damaged peripheral nerve pathways.

This research is concerned with the application of system identification and adaptive control to Functional Electrical Stimulation. The work consists of developing a model which describes EMG (Electromyogram) activity to forearm motion. Although several EMG models presently exist, the goal was to produce a model more suitable for on-line applications while also taking into account the system nonlinearities. The parameters of this model were estimated using a least squares algorithm. The model was tested by simulation and experimentally collected data. The developed model explains well the forearm movement. From the developed model, an adaptive controller was designed using a model reference control scheme. This adaptive controller was used for generating the suitable stimulus pattern. The simulation results showed good tracking and indicated the controllers ability to adapt to changes in the arm's nonlinear gain.

This thesis focuses on the development of a data acquisition and signal processing system for analysis of electromyogram (EMG) signals. The data acquisition system was based on a personal computer and was set up for simultaneous recording of three analog channels. Two of these channels were used to record the EMG signals from the triceps and biceps muscles respectively, and the third channel was used to record the acceleration signals obtained from an accelerometer placed on the subject's arm. The objective of the signal processing was to find some characteristic parameters for the EMG signals, so that these parameters could be used in a microprocessor based system for Functional Electrical Stimulation (FES). Such a system may be useful in the rehabilitation of patients with partial paralysis of limbs as a result of brain damage.